application of AI in gaming

This is a discussion on application of AI in gaming within the General AI Programming forums, part of the Cprogramming.com and AIHorizon.com's Artificial Intelligence Boards category; I am a student doing a small project on
" Comparative Studies on Game Programming Techniques to Design Game Players".
...

application of AI in gaming

I am a student doing a small project on

" Comparative Studies on Game Programming Techniques to Design Game Players".

I need few information regarding how the concepts of GA(genetic algrithm), Fuzzy Logic and Neural Network, work in designing games. It would be better if you only say me the core areas where the above mentioned techniques are used in developing a game.
Please, if you can, mention the challenges or the problems that have been faced by the programmers in game development using the above techniques. Informations on wether there is any on going investigation that could aid the game developers in improving the use of AI in modern computer games would also help me a lot. And how does the above techniques also help to make a game player more intelligent, so that it can become more challenging to the person who is playing against it.

Thanks,
Arif.
<<email snipped by mod>>

Last edited by Salem; 03-28-2007 at 09:29 AM.
Reason: Actually previous one was a mail I did to EA Games, not for the forum..

Although I'd like to believe that neural nets and GA are used alot in games to give a pseudo-learning-type aspect to game players, I'm much more inclined to believe that they're all just alpha-beta trees with pruning, with a little probability in the mix to make it look random.

Quake3 bot AI, which is quite good as far as AI goes (AI is very hard to implement!) uses 'fuzzy logic' and is outlined in a PhD thesis (I've read it, but I don't have the link, just google it).

Fuzzy logic and 'neural networks' seem to overlap in some cases. They have a similar structure and appear quite similar. Neural networks are used with genetic algorithms, where rather than explicitly programming the AI, the behaviors emerge from simulated genes of AI agents. The actual behaviors are (ideally) unpredictable, and over time the behaviors best suited genes for the given situation 'emerge,' without having been explicitely put into code. The challenge is setting up the basic fundamental conditions/genes such that evolution can occur. You should look up SMART (think that's what it's called), where on top of evolving the neural network of AI agents the author wrote a library that also evolves the fundamental structure of the neural network. So, the values in the genes are evolving, but the genes themselves are also evolving (really hard to explain what I mean).

Most AI for computer games is not particularly sophisticated. It's extremely difficult to make AI agents, say, coherent over time (they have no clue what happened in the past, and it's incredibily difficult to make AI agents predict what happens in the future). The goal for most game AI coders is to just make sure the AI works for a small set of conditions and make it so that it doesn't do anything too stupid in front of the player.

The results of the neural network are typically manifested in a state function (or a finite state machine), along with a virtual machine which executes instructions. The instructions can be high or low level (yep, you guessed it, CISC or RISC), and the finite state machine determines which instruction to execute next, which based on:

1- How the AI agent perceives reality (limited number of sensory 'organs,' the AI agent has a cognitive model of reality, as do humans. Human's cognitive model of reality may be said to be manifested in mathematical language/logic)
2- How the AI agent chooses to react based on its perception of reality

This is represented as inputs and outputs of a neural network . If there is a definite correct answer, you can setup training programs which automatically adjust the weights on each pin/node of the neural network, otherwise you simulate death, survival and mating to evolve new AI agents, where theoretically over time healtheir AI agents emerge. the SMART AI library takes this to a higher level of abstraction, where the actual fundamental structure of the neural network evolves, along with the weights.

Here's an example of an instruction I wrote for a virtual machine for my AI. You define how high level, or low level, you choose each instruction to be. The following examples are very high level